We have conducted experiments to verify the effectiveness of our approach to question search.

we have investigated the use of identifying question topic and question focus for search.


From Figure 3, we see that our approach performs best when λ is around 0.7.

our approach tends to emphasize question topic more than question focus.


The ambiguities discussed above may be expected to cause degradation in automatic parsing performance.

sentences containing verb form errors are more likely to yield an "incorrect" parse tree, sometimes with significant differences.


The following statistics are computed: Accuracy The proportion of sentences which, after being treated by the system, have correct verb forms.

(true neg + true pos) divided by the total number of sentences.


Recall Out of all sentences with verb form errors, the percentage whose errors have been successfully corrected by the system.

true pos divided by (true pos + f alse neg + inv pos).


Detection Precision This is the first of two types of precision to be reported, and is defined as follows: Out of all sentences for which the system has hypothesized corrections, the percentage that actually contain errors, without regard to the validity of the corrections.

(true pos + inv pos) divided by (true pos + inv pos + f alse pos).


It attempts corrections only when the word in question is actu-ally tagged as a verb.

it ignores the spurious noun-and adjectival phrases in the parse tree discussed in §3.2, and relies only on the output of the part-of-speech tagger.


Table 5 shows that increasing the number of complete realizations also yields improved BLEU and METEOR scores, as well as more exact matches.

the hypertagger makes possible a more than 6-point improvement in the overall BLEU score on both the development and test sections, and a more than 12-point improvement on the sentences with complete realizations.


However, their log-linear selection models have been adapted from ones used in parsing, and do not condition choices based on features of the input semantics to the same extent.

while they employ a baseline maximum likelihood model that conditions the probability of a lexical entry upon its predicate argument structure (PAS) -that is, the set of elementary predications introduced by the lexical item -this probability does not take into account other elements of the local context, including parents and modifiers, and their lexical predicates.


R: We therefore want the matter re-referred to ourselves.

d: why we want the that matters we to be referred back.


D: That is why we want the that matters we to be referred back.

t: why we would like the matter to be referred back.


T: That is why we would like the matter to be referred back.

h: why we wish that the matter we be referred back.


1 looks as follows: This grammar accepts all trees over Σ except ones in which a node with label 2 is the parent of a node with label 1, because such trees correspond to configurations in which a node with label a z is the parent of a node with label a x , a z and a x are permutable, and 2 > 1.

it will accept the configurations (b), (c), and (e) in Fig.


We present a general framework for vector-based composition which allows us to consider different classes of models.

we present both additive and multiplicative models of vector combination and assess their performance on a sentence similarity rating experiment.


Landmarks were taken from WordNet (Fellbaum, 1998).

they belonged to different synsets and were maximally dissimilar as measured by the Jiang and Conrath (1997) measure.


To this end, we optimized the weights on a small held-out set.

we considered eleven models, varying in their weightings, in steps of 10%, from 100% noun through 50% of both verb and noun to 100% verb.


In our experiments we selected parameters that Kintsch reports as optimal.


We anticipate that more substantial correlations can be achieved by implementing more sophisticated models from within the framework outlined here.

the general class of multiplicative models (see equation 4) appears to be a fruitful area to explore.


Consider the task of named entity recognition (NER).

you are given a corpus of news articles in which all tokens have been labeled as either belonging to personal name mentions or not.


These are the problems we address in this paper.

we develop a novel prior for named entity recognition that exploits the hierarchical feature space often found in natural language domains ( §1.2) and allows for the transfer of information from labeled datasets in other domains ( §1.3).


Although a more thorough investigation needs to be carried out, our analysis indicates that the main reason for this phenomenon is over-smoothing.

by letting the information propagate from the leaf nodes in the hierarchy all the way to the root node, the model loses its ability to discriminate between its features.


This toolkit produces features of the type described in §1.2 and thus was amenable to our hierarchical prior model.

we chose to use the simplest default, out-of-the-box feature generator and purposefully did not use specifically engineered features, dictionaries, or other techniques commonly employed to boost performance on such tasks.


We evaluated the performance of various transfer learning methods on the data and tasks described in §3.1.

we compared our approximate hierarchical prior model (HIER), implemented as a CRF, against three baselines: • GAUSS: CRF model tuned on a single domain's data, using a standard N (0, 1) prior • CAT: CRF model tuned on a concatenation of multiple domains' data, using a N (0, 1) prior • CHELBA: CRF model tuned on one domain's data, using a prior trained on a different, related domain's data (cf.


We have provided evidence that motivates these models on intuitive, theoretical and empirical grounds, and have gone on to demonstrate their effectiveness in relation to other, competitive transfer methods.

we have shown that hierarchical priors allow the user enough flexibility to customize their semantics to a specific problem, while providing enough structure to resist unintended negative effects when used inappropriately.


Inspired by the observation about trigger distribution, we propose a similar hypothesis -one argument role per cluster for event arguments.

each entity plays the same argument role, or no role, for events with the same type in a collection of related documents.


Each entry s ℓ,ℓ ′ is drawn independently, depending on the cluster assignments x ℓ and x ℓ ′ .

s ℓ,ℓ ′ is drawn from a Beta distribution with parameters α = if x ℓ = x ℓ ′ and α = otherwise.


We then propose an image annotation model which can learn from our noisy annotations and the auxiliary documents.

we extend and modify Lavrenko's (2003) continuous relevance model to suit our task.


We use a Bayesian framework for estimating P est (w|s a ).

we assume a beta prior (conjugate to the Bernoulli distribution) for each word: where µ is a smoothing parameter estimated on the development set, b w,s a is a Boolean variable denoting whether w appears in the annotation s a , and N w is the number of latent variables that contain w in their annotations.


A simple way to implement this idea is by re-ranking our k-best list according to a topic model estimated from the entire document collection.

we use Latent Dirichlet Allocation (LDA) as our topic model .


In this paper, we describe a new approach for the collection of image annotation datasets.

we leverage the vast resource of images available on the Internet while exploiting the fact that many of them are labeled with captions.


Since hedge cues cause systems to predict false positive labels, our idea here was to train Maximum Entropy Models for the false positive classifications of our ICD-9-CM coding system using the vector space representation of radiology reports.

we classified every sentence that contained a medical term (disease or symptom name) and caused the automated ICD-9 coder 8 to predict a false positive code was treated as a speculative sentence and all the rest were treated as non-speculative sentences.


3 Noting that a Japanese sentence typically consists of a sequence of case marked NPs and adjuncts, followed by a main verb at the end (or what would be called 'matrix verb' in linguistics), we seek to compress each of the major chunks in the sentence, leaving untouched the matrix verb, as its removal often leaves the sentence unintelligible.

starting with the leftmost BS in a major constituent, 3 Example 3 could be broken into BSs: / Mushuku -no / John -ga / takai / kuruma -wo / kat-ta /.


As was demon-strated in Titov and McDonald (2008), the topics produced by LDA do not correspond to ratable aspects of entities.

these models tend to build topics that globally classify terms into product instances (e.g., Creative Labs Mp3 players versus iPods, or New York versus Paris Hotels).


(2008) also showed that joint models of text and user annotations benefit extractive summarization.

they used signals from pros-cons lists whereas our models use aspect rating signals.


We then run a number of parsing experiments using our new version of the CCGbank corpus.

we implement new features using NER tags from the BBN Entity Type Corpus (Weischedel and Brunstein, 2005).


We treat the correct simplification as a hidden variable, using labeled SRL data to guide us towards "more useful" simplifications.

we train our model discriminatively to predict the correct role labeling assignment given an input sentence, treating the simplification as a hidden variable.


Also, for a sentence s there may be several simple labelings that lead to the same role labeling.

there may be several simple labelings which assign the correct labels to all constituents; we denote this set K sv ⊆ C sv .


Another group of related work focuses on summarizing sentences through a series of deletions (Jing, 2000;Dorr et al., 2003;Galley & McKeown, 2007).

the latter two works iteratively simplify the sentence by deleting a phrase at a time.


We use the wellknown lexical database WordNet to get the semantic similarity of two words.

we use the package by (Pedersen et al., 2004), which includes several methods to compute the semantic similarity.


The weight of an edge (s, t) can be any of the three metrics described in the previous section.

when the weight of the edge is based on clue words as in Equation 1, this method is equivalent to Algorithm CWS in (Carenini et al., 2007).


In this way, the ROUGE metric measures the similarity of a system generated summary to a gold standard summary that is considered important by most human summarizers.

we choose ROUGE-2 and ROUGE-L as the evaluation metric.


In contrast, when the weight of edges are computed based on clue words, the average runtime to compute the SentScore for all sentences in a conversation is only 0.05 seconds, which is even a little less than the time to compute the semantic similarity of one pair of words.

when CWS has generated the summary of one conversation, we can only get the semantic distance between one pair of words.


Thus, we need to carefully consider the applicability of rules in a treebank to new text.

we need to examine ad hoc rules, rules used for particular constructions specific to one data set and unlikely to be used on new data.


Since only 1% of tokens have an IWER > 1, we simplify modeling by predicting only whether each token is responsible for an error or not.

our dependent variable is binary, taking on the value 1 if IWER > 0 for a given token and 0 otherwise.


This last result sheds some light on the work of Adda-Decker and Lamel (2005), who suggested several factors that could explain males' higher error rates.

they showed that males have higher rates of disfluency, produce words with slightly shorter durations, and use more alternate ("sloppy") pronunciations.


As shown in Table 3, speaker identity is a very important factor in determining the probability of error.

the lexical and prosodic variables examined here are not sufficient to fully explain the differences in error rates between speakers.


For the English translations of the NEs, BBN annotators looked at human reference translations, which may introduce a bias towards those human translations.

the BBN annotations are sometimes wrong, because the reference translations were wrong.


We present our adap-Sentence → Words Words → Word Words → Word Words Word → Phonemes Phonemes → Phoneme Phonemes → Phoneme Phonemes  tor grammars using regular expressions for clarity, but since our implementation does not handle regular expressions in rules, in the grammars actually used by the program they are expanded using new non-adapted nonterminals that rewrite in a uniform right-branching manner.

the adaptor grammar used by the program is shown in Figure 2.


Inspection of the parses shows that the unigram syllable grammar also tends to misanalyse long collocations as Words.

it seems to misanalyse function words as associated with the content words next to them, perhaps because function words tend to have simpler initial and final clusters.


6 We then split the matrix for the model parameters, p(n|c) and p(vt|c), along with the class coordinate.

each cluster node is responsible for storing only a part of classes C l , i.e., 1/|P | of the parameter matrix, where P is the number of cluster nodes.


The richness and complexness of Chinese abbreviations imposes challenges to the SMT systems.

many Chinese abbreviations may not appear in available parallel corpora, in which case current SMT systems treat them as unknown words and leave them untranslated.


Though one can exploit a sophisticated named-entity tagger to extract English entities, in this paper we identify English entities based on the capitalization information.

to be considered as an entity, a continuous span of English words must satisfy the following conditions: • all words must start from a capital letter except for function words "of", "the", and "and"; • each function word can appear only once; • the number of words in the span must be smaller than a threshold (e.g., 10); • the occurrence count of this span must be greater than a threshold (e.g., 1).


for abbr in sent2 9 if RL(full, abbr ) = TRUE 10 Count[abbr , full]++ 11 return Count Given a monolingual corpus and a list of full-form phrases (i.e., F ull-list, which is obtained in Section 3.2), the algorithm returns a Count that contains full-abbreviation relations and their occurrence counts.

the algorithm linearly scans over the whole corpus as indicated by line 1.


With the count table Count, we can calculate the relative frequency and get the following probability, Given a Chinese abbreviation and its full-form, we induce English translation entries for the abbreviation by using the full-form as a bridge.

we first generate n-best translations for each fullform Chinese phrase using the baseline system.


Since the obtained translation entries for abbreviations have the same format as the regular translation entries in the baseline phrase table, it is relatively easy to add them into the baseline phrase table.

if a translation entry (signatured by its Chinese and English strings) to be added is not in the baseline phrase table, we simply add the entry into the baseline table.


Recently, Chang and Lai (2004), Chang and Teng (2006), and Lee (2005) have investigated this task.

chang and Lai (2004) describes a hidden markov model (HMM) to model the relationship between a full-form phrase and its abbreviation, by treating the abbreviation as the observation and the full-form words as states in the model.


Nevertheless, it is worthwhile testing whether the meaning components that are brought out by syntactic alternations are also correlated to the neighboring words.

levin verbs may be distinguished on the dimension of neighboring words, in addition to argument structures.


Although there is not a clear winner, DR and SCF+CO generally outperform other feature sets, indicating that they are effective ways for combining syntactic and lexical information.

these two feature sets perform comparatively well on the tasks that involve more classes (e.g.


Our first new approach for handling differences in transcripts is an extension of the logistic regression model previously used in data fusion work, (Savoy et al., 1988).

we augment the model with the first-order interaction of scores x 1 x 2 and the factor α i , so that where the relevance R i ∼ Binomial.


We apply the same reasoning to test for statistical significance in GMAP improvements.

we test for significant improvements in GMAP by applying the Wilcoxon signed rank test to the paired, transformed average precisions, log AP.


We hypothesize that these users will need cooperative responses that select and generalize the information provided.

we hypothesize that a particular type of cooperative response, intensional summaries, when provided incrementally during a dialogue, are effective for large or complex domains, or when users are unfamiliar with the domain.


The first rule is very simple, simply stating that, if the highest utility value of the Ref-Sing response is lower than a particular threshold, then use the UM-Assoc response.

if one of the two highest scoring response types has a low utility, use the other.


We define context words as those which occur in proximity of a NE.

context words are the words present in the w i−2 , w i−1 , w i+1 or w i+2 position if w i is a NE.


In this paper, we compare the same web-based approach to acquiring stereotypical concept descriptions from text using two very different languages, English and Chinese, to determine the extent to which the same cross-cultural knowledge is unearthed for each.

we treat the web as a large parallel corpus (e.g., see Resnick and Smith, 2003), though not of parallel documents in different languages, but of corresponding translationequivalent phrases.


Veale and Hao (2007) report that when the above 42,618 simile types are hand-annotated by a native speaker, only 12,259 were judged as non-ironic and meaningful in a null context.

just 29% of the retrieved pairings conform to what one would consider a well-formed and reusable simile that conveys some generic aspect of cultural knowledge.


In those mapped, we find 3194 English similes and 4019 Chinese similes that were hand-annotated as valid by their respective native-speaker judges.

translation equivalence can be used to separate well-formed stereotypical beliefs from illformed or ironic beliefs with approximately 80% precision.


An alternative approach is proposed by Xu and Schuurmans (2005) who formulate a semi-definite programming (SDP) approach.

they present an algorithm for multiclass unsupervised and semi-supervised SVM learning, which relaxes the original non-convex objective into a close convex approximation, thereby allowing a global solution to be obtained.


Thus, in this paper, we focus on semi-supervised language learning, where we can make use of both labeled and unlabeled data.

we investigate a semi-supervised approach for structured large margin training, where the objective is a combination of two convex functions, the structured large margin loss on labeled data and the least squares loss on unlabeled data.


The parser's goal is then to find the most preferred parse; that is, a projective tree, Y ∈ Φ (X), that obtains the highest "score".

one would assume that the score of a complete spanning tree Y for a given sentence, whether probabilistically motivated or not, can be decomposed as a sum of local scores for each link (a word pair) (Eisner, 1996;Eisner and Satta, 1999;McDonald et al., 2005a).


Supervised structured large margin training approaches have been applied to parsing and produce promising results (Taskar et al., 2004;McDonald et al., 2005a;Wang et al., 2006).

structured large margin training can be expressed as minimizing a regularized loss (Hastie et al., 2004), as shown below: where Y i is the target tree for sentence X i ; L i,k ranges over all possible alternative k trees in , as shown in Section 2; and ∆(L i,k , Y i ) is a measure of distance between the two trees L i,k and Y i .


Given a convex approach to semi-supervised structured large margin training, and an efficient training algorithm for achieving a global optimum, we now investigate its effectiveness for dependency parsing.

we investigate the accuracy of the results it produces.


The PropBank corpus is the result of adding a semantic layer to the syntactic structures of Penn Treebank II (Palmer et al., 2005).

it pro-vides information about predicate-argument structures to all verbal predicates of the Wall Street Journal section of the treebank.


In this paper we apply structured SVMs to the syllabification problem.

we formulate syllabification as a tagging problem and apply the SVM-HMM software package 1 (Altun et al., 2003).


The numbered NB and ONC systems make many of the same errors, on words that we might expect to cause difficulty.

both suffer from being unaware of compound nouns and morphological phenomena.


Since the harmonic mean is a non-linear combination, we can not optimize the F-scores on sub-forests independently with a greedy algorithm.

the optimal F-score tree in a forest is not guaranteed to be composed of two optimal F-score subtrees.


Finally, we found it useful to impose a form of vocabulary restriction on the cluster-based features.

for any feature that is predicated on a word form, we eliminate this feature if the word in question is not one of the top-N most frequent words in the corpus.


Heavily lexicalised grammars have been used in applications such as machine translation and information extraction because they can produce semantic structures which provide more information than less informed parsers.

because of the structural and semantic information attached to lexicon items, these grammars do well at describing complex relationships, like non-projectivity and center embedding.


When POS tags are used to restrict the parser, any lexicon entry that unifies with the generic part-of-speech lexical category can be used by the parser.

when the word is restricted to, for example, a verb, any lexical item with one of the numerous more specific verb categories can be used.


Given that more human-like is better, both rankings support our hypothesis that the quality of the models from the best to the worst is: real, clu, cor, and ran.

although it is hard to obtain well-agreed ratings among judges, we can combine the judges' ratings to produce the ranking of the models.


For each dialog, a ranking score is calculated as: In the training phase, the human ratings are used to set α by minimizing the loss function: The eval function returns 0 if (x, y) pair is ranked correctly, and 1 otherwise.

lOSS score is the percentage of misordered pairs where the order of the predicted scores disagree with the order indicated by human judges.


G is composed of nodes (v) which correspond to possible intermediate steps in the process of completing the specified task, and edges (e) which con- nect nodes.

v corresponds to user goal state to achieve domain-specific subtask in its expected agenda.


In a WOZ experiment, a hidden human operator, the so called "wizard", simulates (partly or com-pletely) the behaviour of the application, while subjects are left in the belief that they are interacting with a real system (Fraser and Gilbert, 1991).


We learn with the decision tree J4.8 classifier, WEKA's implementation of the C4.5 system (Quinlan, 1993), and rule induc-baseline JRip J48 timing 52.0(± 2.2) 50.2(± 9.7) 53.5(±11.7) modality 51.0(± 7.0) 93.5(±11.5)* 94.6(± 10.0)* Table 1: Predicted accuracy for presentation timing and modality (with standard deviation ±), * denotes statistically significant improvement at p < .05 tion JRIP, the WEKA implementation of RIPPER (Cohen, 1995).

we learn models which predict the following wizard actions: • Presentation timing: when the 'average' wizard starts the presentation phase • Presentation modality: in which modality the list is presented.


We then compare quantitative dialogue measures performing a paired t-test.

we compare mean values of the final rewards, number of filled and con-firmed slots, dialog length, and items presented multimodally (MM items) and items presented verbally (verbal items).


In this paper, we show that one can in fact partly recover the order information.

given a set of documents in unigram-count BOW representation, one can recover a non-trivial bigram language model (LM) 1 , which has part of the power of a bigram LM trained on ordered documents.


The dissimilarity is 0 if θ = φ, and increases as they diverge.

the KLdivergence between two word distributions conditioned on the same history u is We define D(φ, θ) to be the average KL-divergence over all histories: , which is convex in θ (Cover and Thomas, 1991).


This means p j (x j , y) can be factorized by the cliques c in G(x, y).

p j (x j , y; θ j )= c p j (x jc , y c ; θ j ).


An example of non-convergence is the oscillation of the estimated Θ.


On the other hand, ASO-semi and JESS-CM have an important common feature.

both meth-ods discriminatively combine models trained by using unlabeled data in order to create informative feature representation for discriminative learning.


Table 1 shows that the correlation between the labels of contiguous sentences is significant.

when a sentence Y t 's previous Y t−1 is not a context (Y t−1 = C) then it is very likely that Y t (i.e.


To label S10, we need consider the dependency relation between Q2 and Q3.

the question-answer relation between Q3 and S10 can be captured by a joint modeling of the dependency among S10, Q2 and Q3.


For this reason we choose as a ranking algorithm the Perceptron which is both accurate and efficient and can be trained with online protocols.

we implement the ranking Perceptron proposed by Shen and Joshi (2005), which reduces the ranking problem to a binary classification problem.


In this paper we investigate how this powerful source of information can be exploited for unsupervised language learning.

we study the task of morphological segmentation of multiple languages.


For each new phrase-pair, the model first chooses the number and type of morphemes to be generated.

it must choose how many unaligned stray morphemes from language E, unaligned stray morphemes from language F, and abstract morphemes are to compose the parallel phrases.


However, if the languages E and F are related and the regular phonetic correspondences between the letter in the two alphabets are known, then we can use P to assign higher likelihood to potential cognates.

we define the prior P (e, f ) to be the probabilistic string-edit distance (Ristad and Yianilos, 1998) between e and f , using the known phonetic correspondences to parameterize the string-edit model.


In particular we define the prior P (e, f ) to be the probabilistic string-edit distance (Ristad and Yianilos, 1998) between e and f , using the known phonetic correspondences to parameterize the string-edit model.

insertion and deletion probabilities are held constant for all characters, and substitution probabilities are determined based on the known sound correspondences.


m, n, k ∼ P oisson(λ) Given these values, we now draw the appropriate number of stray and abstract morphemes from the corresponding distributions: The sets of morphemes drawn for each language are then ordered: Finally the ordered morphemes are fused into the words that form the parallel phrases: To keep the model as simple as possible, we employ uniform distributions over the sets of orderings and fusings.

given a set of r morphemes (for each language), we define the distribution over permutations of the morphemes to simply be ORDER(•|r) = 1 r!


While we demonstrate competitive results using a simple suffix-based ambiguity-class guesser which ignores capitalization and hyphenation information, we believe there is much room for improvement in this respect.

(Haghighi and Klein, 2006) presents very strong results using a distributional-similarity module and achieve impressive tagging accuracy while starting with a mere 116 prototypical words.


In Figure 4 we can see an example of how the person tag is extracted from the subject of the sen- tence and gets passed to the relative clause.

as the algorithm parses the syntax tree, it identifies the sub-tree which has NP-A as a head and includes the WHNP node.


Our method focuses on two linguistic phenomena which produce common errors on the output and are important constituents of the sentence.

noun cases and verb persons are required by the target language, but not directly inferred by the source.


In our model, they are related through a vector z i,j ∈ R d representing the shared, language-independent concept.

to generate the feature vectors, we first generate a random concept The source feature vector f S (s i ) is drawn from a multivariate Gaussian with mean W S z i,j and covariance Ψ S , where W S is a d S × d matrix which transforms the language-independent concept z i,j into a languagedependent vector in the source space.


Thus, we adopt a bootstrapping-style approach that only permits high confidence edges at first, and then slowly permits more over time.

we compute the optimal full matching, but only retain the highest weighted edges.


We use the following corpora: Note that even when corpora are derived from parallel sources, no explicit use is ever made of document or sentence-level alignments.

our method is robust to permutations of the sentences in the corpora.


They assumed that if two English phrases e 1 and e 2 are aligned with the same phrase c in another language, these two phrases may be paraphrases.

they computed the paraphrase probability in terms of the translation probabilities: are the probabilities of translating e 1 to c and c to e 2 , which are computed based on MLE: where count(c, e 1 ) is the frequency count that phrases c and e 1 are aligned in the corpus.


The other reason is that DIRT can only be effective for patterns appearing plenty of times in the corpus.

it seriously suffers from data sparseness.


Given s in d and the citation context C, we would first estimate θ s based on s and estimate θ I based on C, and then score s with the negative KL divergence of θ s and θ I .

where V is the set of words in our vocabulary and w denotes a word.


To achieve this balance, we treat the citation context C as prior information and the current document d as the observed data, and use Bayesian estimation to estimate the impact language model.

let p(w|C) be a citation context language model estimated based on the citation context C. We define Dirichlet prior with parameters {µ C p(w|C)} w∈V for the impact model, where µ C encodes our confidence on this prior and effectively serves as a weighting parameter for balancing the contribution of C and d for estimating the impact model.


An expert in Information Retrieval field read each paper and its citation context, and manually created an impact-based summary by selecting all the "impactcapturing" sentences from the paper.

the expert first attempted to understand the most influential content of a paper by reading the citation contexts.


In general, for a wide range of values of these parameters, the performance is relatively stable and near optimal.

the performance is near optimal as long as µ s and µ c are sufficiently large (µ s ≥ 1000, µ c ≥ 20, 000), and the interpolation parameter δ is between 0.4 and 0.9.


It also differs from regular multi-document summarization because the roles of original documents and citation contexts are not equivalent.

citation contexts serve as an indicator of the impact of the paper, but the summary is generated by extracting the sentences from the original paper.


All of the previous approaches treat the problem as one of clustering speakers, rather than utterances.

they assume that during the window over which the system operates, a particular speaker is engaging in only one conversation.


This is not a statistic to be optimized (indeed, optimization is trivial: simply make each utterance in the source into its own conversation), but it can give us some intuition about specificity.

if one subdivides a coarse-grained annotation to make a more specific variant, the many-to-one accuracy from fine to coarse remains 1.


For simplicity, the framework considered for the entitymention model adopts similar training and testing procedures as for the mention-pair model.

a training or testing instance has the form of i{e i , m j }, in which m j is an active mention and e i is a partial entity found before m j .


The features are similar to those for the mention-pair model as shown in Table 2, but their values are calculated at an entity level.

the lexical and grammatical features are computed by testing any mention 1 in the entity against the active mention, for ex-1 Linguistically, pronouns usually have the most direct coref-ample, the feature nameAlias is assigned value 1 if at least one mention in the entity is a name alias of the active mention.


Given a document, we encode a mention or a partial entity with a unique constant.

m j represents the jth mention (e.g., m 6 for the pronoun "he").


This paper empirically demonstrates that gesture correlates with discourse structure.

we show that automatically-extracted visual features can be combined with lexical cues in a statistical model to predict topic segmentation, a frequently studied form of discourse structure.


This is in contrast to NE-AL which performs worse than random selection for all corpora when used as extrinsic selection, and for PARSE-AL that outperforms the random baseline only for Brown when used as extrinsic selection.

the MTAL protocols suggest a tradeoff between the annotation efforts of the different tasks, here.


GE criteria are terms in a training objective function that assign scores to values of a model expectation.

we use a version of GE that prefers parameter settings in which certain model expectations are close to target distributions.


GE criteria can use more information than these previous methods.

gE criteria can take advantage of conditional probability distributions of labels given a feature (p(y|f k (x) = 1)).


This data differs slightly from the tokenization used by CRR07.

it lacks the newline breaks which might be a useful piece of information.


12 Assuming that labeling one token in isolation takes the same time as labeling one token in a sequence, these results strongly support a new paradigm of labeling in which instead of annotating entire sentences, the human instead selects some key features of interest and labels tokens that have this feature.

intriguing is the flexibility our scenario provides for the selection of "features of interest" to be driven by error analysis.


Both models reduced the large search space by imposing strong restrictions on the form of search candidates.

ng and Low (2004) used character-based POS tagging, which prevents some important POS tagging features such as word + POS tag; Shi and Wang (2007) used an n -best reranking approach, which limits the influence of POS tagging on segmentation to the n -best list.


In this paper, we propose conducting expert search with a fine-grained approach.

we utilize more specific evidences existing in the documents.


Comparison with other systems 6 This paper proposed to conduct expert search using a fine-grained level of evidence.

quadruple evidence was formally defined and served as the basis of the proposed model.


Another notable thing about our results is that they seem to be good enough for practical applications.

our one-best selection (over all tried conditions) is correct at least 82% of the time over all collections, including the largest one.


Each training instance (c, t) is represented by a feature vector f (c, t) ∈ R k , where features are defined in terms of arbitrary properties of the configuration c, including the state of the stack σ c , the input buffer β c , and the partially built dependency graph G c .

many features involve properties of the two target tokens, the token on top of the stack σ c (σ 0 c ) and the first token in the input buffer β c (β 0 c ), which are the two tokens that may become connected by a dependency arc through the transition out of c. The full set of features used by the base model MaltParser is described in Nivre et al.


Secondly, both the guided models have better precision than their base model and, for the most part, also their guide model.

mST malt outperforms mST and is comparable to malt for short arcs.


Perhaps the most striking feature of Wambaya is its word order: it is a radically non-configurational language with a second position auxiliary/clitic cluster.

aside from the constraint that verbal clauses require a clitic cluster (marking subject and object agreement and tense, aspect and mood) in second position, the word order is otherwise free, to the point that noun phrases can be non-contiguous, with head nouns and their modifiers separated by unrelated words.


While further development of the Wambaya grammar might make use of some of the remaining 41% of the types, this work suggests that there is a substantial amount of information in the Matrix core type hierarchy which would better be stored as part of the typological libraries.

the analyses of argument realization implemented in the Matrix were not used for this grammar.


This idea was previously proposed by (Matusov et al., 2004;Liang et al., 2006) although the the objectives differ.

consider a feature that takes on value 1 whenever source word i aligns to target word j in the forward model and -1 in the backward model.


In this section we attempt to investigate whether our improved alignments produce improved machine 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8  translation.

we fix a state of the art machine translation system 1 and measure its performance when we vary the supplied word alignments.


Commonly, an author might use such a parenthetical when a given term has no standard translation (or transliteration), and does not appear in conventional dictionaries.

an author might expect a term to be an out-of-vocabulary item for the target reader, and thus helpfully provides a reference translation in situ.


We made a small change to Competitive Linking to allow consecutive sequence of words on one side to be linked to the same word on the other side.

instead of requiring both e i and f j to have no previous linkages, we only require that at least one of them be unlinked and that (suppose e i is unlinked and f j is linked to e k ) none of the words between e i and e k be linked to any word other than f j .


For the systems using word lattices, the training data contained the versions of the corpus appropriate for the segmentation schemes used in the input.

for the hs+ss condition, the training data consisted of two copies of the corpus: one segmented with the Harbin segmenter and the other with the Stanford segmenter.


(1993), which identified the optimal paraphrase T * of a sentence S by finding: In contrast, we adopt a log-linear model (Och and Ney, 2002) in this work, since multiple paraphrase tables can be easily combined in the loglinear model.

feature functions are derived from each paraphrase resource and then combined with the language model feature 1 : where N is the number of paraphrase tables.


Thus we employ a new approach for paraphrase extraction.

we parse the sentences with CollinsParser 5 and extract the chunks from the parsing results.


In this paper we present an algorithm for comma resolution, a task that we define to consist of (1) disambiguating comma type and (2) determining the relations entailed from the sentence given the commas' interpretation.

in (1) we assign each comma to one of five possible types, and in (2) we generate a set of natural language sentences that express the relations, if any, signified by each comma structure.


We demonstrate that a system for contradiction needs to make more fine-grained distinctions than the common systems for entailment.

we argue for the centrality of event coreference and therefore incorporate such a component based on topicality.


This drop in accuracy on the test sets is greater than that of many RTE systems, suggesting that generalizing for contradiction is more difficult than for entailment.

when addressing contradictions that require lexical and world knowledge, we are only able to add coverage in a piecemeal fashion, resulting in improved performance on the development sets but only small gains for the test sets.


A new approach is now proposed, which consists of training with new data, but starting with previously calculated models.

previously trained models provide initialized models for the new train.


When the transition from previous word to current word is lowprobability, from the parser's perspective, the surprisal is high and the psycholinguistic claim is that behavioral measures should register increased cognitive difficulty.

rare parser actions are cognitively costly.


While vagueness exists at the conceptual level, ambiguity lies at the level of linguistic expressions.

an ambiguous statement contains linguistic expressions that can refer to multiple explanations, whereas a vague statement carries a concept with unclear or soft definition.


PRED is a general function that takes a ranker and a M N-best lists and generates a set of M N -dim output vector y representing the predicted reciprocal rank.

suppose a 3-best list and a ranker predicts ranks (1,3,2) for the 1st, 2nd, and 3rd hypotheses, respectively.


Research on coreference resolution and summarization has modeled the way entities are realized as concrete phrases in discourse.

there exist models of the noun phrase syntax used for discourse-new versus discourse-old referents, and models describing the likely distance between a pronoun and its antecedent.


Note that this model is degenerate, because it assigns probability mass to nonsensical clusterings.

it will allow Prior work (Soon et al., 2001; Denis and Baldridge, 2007) has generated training data for pairwise classifiers in the following manner.


In our future work, we will perform more detailed statistical analyses of user behaviour.

we will analyse the effect of dialogue strategies on behaviour, experiment with different Bayesian network structures, and use the resulting user simulations to learn dialogue strategies for both older and younger users as another way for testing the accuracy of our user models and validating our results.


This hypothesis assumes that in natural languages, there is a tendency for an author to be consistent in the same discourse or article.

if in a specific discourse, an ambiguous phrase or term has a specific meaning, any other subsequent instance of this phrase or term will have the same specific meaning.


This dataset includes 564,554 words where 114,814 of them are abbreviations instances, and 42,687 of them are ambiguous.

about 7.5% of the words are ambiguous abbreviations.


Generally, contextual features based on the context that comes after the abbreviation, achieve considerably better results than all other contextual features.

the A1WC_osWC feature variant achieves the best result with 87.75% accuracy.


An alternative to tercom, considered in this paper, is to use the Inversion Transduction Grammar (ITG) formalism (Wu, 1997) which allows one to view the problem of alignment as a problem of bilingual parsing.

iTGs can be used to find the optimal edit sequence under the restriction that block moves must be properly nested, like parentheses.


Specifically, ITGs can be used to find the optimal edit sequence under the restriction that block moves must be properly nested, like parentheses.

if an edit sequence swaps adjacent substrings A and B of the original string, then any other block move that affects A (or B) must stay completely within A (or B).


This program was further sped up (by about a factor of 2) with an A * search heuristic computed by additional code.

our admissible outside heuristic for aligning two substrings estimated the cost of aligning the words outside those substrings as if reordering those words were free.


We compared alignments obtained using the widely used program tercom with alignments obtained with ITGs and we established that the ITG alignments are superior in two ways.

: (a) we showed that invWER (computed using the ITG alignments) gives a better approximation to TER between machine translation outputs and human references than tercom; and (b) in an oracle system combination experiment, we found that confusion networks generated with ITG alignments contain better oracles, both in terms of tercomTER and in terms of BLEU.


Classification Algorithms: We experimented with a variety of classifiers in the Weka framework (Witten and Frank, 2005).

we compared Support Vector Machines, Decision trees, and Boosting-based classifiers.


We now describe our initial attempt at personalizing the ASP framework described above to each asker: • ASP Pers+Text: We first consider the naive personalization approach where we train a separate classifier for each user.

to predict a particular asker's satisfaction with the provided answers, we apply the individual classifier trained solely on the questions (and satisfaction labels) provided in the past by that user.


We want to predict, for a given user and their current question whether the user will be satisfied, according to our definition in Section 2.

our "truth" labels are based on the rating subsequently given to the best answer by the asker herself.


Here we apply this technique to parser adaptation.

we self-train the standard Charniak/Johnson Penn-Treebank parser using unlabeled biomedical abstracts.


In this paper we apply this technique to parser adaptation.

we self-train the standard Charniak/Johnson Penn-Treebank (C/J) parser using unannotated biomedical data.


In section two, we look at previous work.

we note that there is, in fact, very little data on self-training when the corpora for self-training is so different from the original labeled data.


This paper is based on the C/J parser and thus its results are much more in line with modern expectations.

it was able to achieve an f -score of 87% on Brown treebank test data when trained and selftrained on WSJ-like data.


We design a classifier based on SVMs and different kernels applied to several forms of question and answer representations: (1) linear kernels on the bag-of-words (BOW) or bag-of-POS-tags (POS) features, (2) the String Kernel (SK) (Shawe-Taylor and Cristianini, 2004) on word sequences (WSK) and POStag sequences (POSSK), (3) the Syntactic Tree Kernel (STK) (Collins and Duffy, 2002) on syntactic parse trees (PTs), (4) the Shallow Semantic Tree Kernel (SSTK) (Moschitti et al., 2007) and the Partial Tree Kernel (PTK) (Moschitti, 2006) on PASs.

pOS-tag sequences and pAS trees used with SK and pTK yield to two innovative kernels, i.e.


These metrics correspond in a one-to-one manner to the seven different disambiguation tasks discussed in Section 2, and we use the task name for the evaluation label.

the MorphPOS label is positive if the analysis has the same POS value as the correct analysis in the gold standard; the LexChoice label provides the same information about the lexeme choice.


2 The basic premise of the heuristic is that the text currently being read provides an approximation of the current user interest.

as a user reads a sentence, it potentially represents a fine-grained information need.


With the increasing success of machine translation (MT) in recent years, several researchers have suggested transferring similar methods for monolingual text rewriting tasks.

knight and Marcu (2000) (kM) applied a channel model to the task of sentence compression -dropping words from an individual sentence while retaining its important information, and without sacrificing its grammaticality.


In subsequent back off levels, we abstract away from the CFG rules.

level 5 estimates the probability of dropping subtrees in the context of a certain parent and head child, and level 6 estimates the probability of the same outcome in the coarser context of a parent only.


However, a modified estimate is used for lower order n-grams used for back-off.

the interpolated form is: Pr(c|ab) = α(c|ab) + γ(ab)Pr (c|b) (5) Pr (c|ab) = α (c|ab) + γ (ab)Pr (c|b) the modified estimate Pr (c|b) for a lower order n-gram is taken to be proportional to the number of unique words that precede the n-gram in the training data.


For example, the following are all possible event arguments: • the U.S. invasion of Iraq • Red Cross admitting Israeli and Palestinian groups • GM offers buyouts to union employees In this paper, we describe a method to match an event query argument to the sentences that mention that event.

we seek to model p(s contains e | s, e), where e is a textual description of an event (such as an event argument for a GALE distillation query) and where s is an arbitrary sentence.


These are similar to the "bag-ofwords" features common to many IR and questionanswering systems.

we compute the value overlap(s, e) = w s •w e |w e | 1 , where w e (resp: w s ) is the {0,1}-valued word-feature vector for the event (resp: sentence).


Figure 2 shows these four types of features using the event of "Abdul Halim Khaddam resigns as Vice President of Syria" and the sentence "The resignation of Khaddam was abrupt" as an example.

the "depth" features attempt to capture the "importance" the dependency match, as measured by the depth of the ancestor in the event dependency tree.


It is also worthwhile noting that in such populations, out of 100 best evolved measures, all but four of them featured a condition identical to that of M 13 (POS (b) = X).

the majority of the measures evolved this condition completely independently, without H being included in the initial population.


Next, we use all negative examples to find G x i and G y i which are generalization types of the arguments of a given positive example < x i , y i >.

we perform generalization per relation argument in a form of one positive example vs. all negative examples.


To avoid this, we propose to generalize semantic category of one argument by taking into account a semantic category of the other.

one can represent a binary relation as a bipartite graph where the corresponding nodes (relation arguments) are connected.


One possibility, which we explore in this paper, is that it is the generic nature of the fusion which causes problems.

we hypothesize that fusing two sentences in the context of a preceding question (the natural setting in QA applications) results in more agreement among humans.


Since a feature set mainly based on word frequencies can produce state-of-the-art summaries, we need to analyze further the current set-up for the query-based multi-document summarization task.

we need to ask the question whether the selection of relevant documents for the DUC topics is in any way biased.


Because dependency grammars are reduced to CFGs, any learning algorithm developed for PCFGs can be applied to them.

we use the Inside-Outside algorithm defined in (Pereira and Schabes, 1992) to learn transformed dependency grammars annotated with hidden symbols.


In natural language applications, the size |SV | tends to be very large (Isozaki and Kazawa, 2002), often above 10,000.

the size of the support vectors set can grow linearly with the number of training examples, of which there are usually at least tens of thousands.


We compare the performance of our methods with the algorithm in (Fragkou et al., 2004) on our test set.

the similarity representation is a main difference between those two methods.


In their approach, images associated with textual features are used for clustering, so that the text and images are clustered at the same time.

spectral clustering is applied to the distance matrix built from a multimodal feature set associated with the images to get a better feature representation.


The experimental result of the role classification task showed a 19.16% of the error reduction and a 7.42% improvement in the macroaveraged F1 score.

the method we have presented was able to classify roles having few instances.


The parameter α may be selected differently for each of the argument representations.

in order to avoid using unreliable statistics, we apply this for a given pair only if nx•ny N > r, for some parameter r. we consider P M i(x, y) to be reliable, only if the denominator in equation 2is sufficiently large.


This is supported by our experimental results, the setup and details of which we give in Sections 4-10.

using CCG enables us to map semantic roles directly onto verbal categories, an innovation of our approach that leads to performance gains (Section 7).


In order to generate examples of the argument mapping for training purposes, it is necessary to employ the PARG relations for a given sentence to identify the headwords of each of the verbal arguments.

we use the PARG relations to identify the headwords of each of the constituents that are arguments of the verb.


For grammar formalism conversion, we choose the DS to PS direction for the convenience of the comparison with existing works (Xia and Palmer, 2001;Xia et al., 2008).

we assume that the source grammar formalism is dependency grammar, and the target grammar formalism is phrase structure grammar.


We perform experiments on different subsets of training and testing data based on the sentencelength.

we experimented with sentence length limits of 10, 20, and 30 for both the training and testing sets.


Instead, policy gradient algorithms employ stochastic gradient ascent by computing a noisy estimate of the expectation using just a subset of the histories.

we draw samples from p(h|θ) by acting in the target environment, and use these samples to approximate the expectation in equation 4.


Given these examples, it is straightforward to construct a reward function that connects policy gradient to maximum likelihood.

define a reward function r(h) that returns one when h matches the annotation for the document being analyzed, and zero otherwise.


We model + and − as geometric distributions.

5 In 4 While a more sophisticated model of words would be useful if we intended to use this model for natural language generation, the false independence assumptions present here matter less for the task of learning the semantic correspondences because we always condition on w. 5 p( +; α+) = (1 − α+) + −1 α+, where α+ is a field-specific parameter; p( −; α−) is defined analogously.


Our goal is in estimating the "essential" size of the vocabulary Ω using only the observations.

having seen a sample of size n we wish to know, given another sample from the same population, how many unseen elements we would expect to see.


The authors in (Baayen, 2001;Khmaladze and Chitashvili, 1989;Wagner et al., 2006) propose a natural scaling formulation to study this problem; specifically, (Baayen, 2001) has a tutorial-like summary of the theoretical work in (Khmaladze, 1987;Khmaladze and Chitashvili, 1989).

in particular, the authors consider a sequence of vocabulary sets and probability distributions, indexed by the observation size n. the observation (X 1 , .


Thus, the object of our interest is, One of the main results of (Wagner et al., 2006) is that the scaled profiles converge to a deterministic probability vector under the scaling model introduced in Section 3.3.

we have from Proposition 1 of (Wagner et al., 2006): where This convergence result suggests a natural estimator for O n , expressed in Equation (6).


We generate training (s, t) pairs in the obvious way from our stressmarked training words,w.

we first extract the letter/phoneme portion, w, and use it to create the substrings, s. We then create the stress pattern, t, usingw's stress markers.


At test time we simply score each possible output pattern using the learned weights.

for an input sequence s of length N , we compute λ • Φ(s, t) for all t ∈ T N , and we take the highest scoring t as our output.


Instead, we adopt a less intrusive strategy, which we refer to as "context ordering," that biases the decision tree toward letters that are closer to the focus, but permits gaps when the information gain for a distant letter is relatively high.

the ordering constraint described above is still applied, but only to letters that have aboveaverage information gain (where the average is calculated across all letters/attributes).


Instead of relying solely on the particular letters that surround the focus letter, we postulate that the learner could achieve better generalization if it had access to information about the types of letters that appear before and after.

instead of treating letters as abstract symbols, we would like to encode knowledge of the similarity between certain letters as features.


The alignment can be performed via the Expectation-Maximization (EM) by starting with a random initial alignment and calculating the affinity matrix count(e i , c j ) over the whole parallel corpus, where element (i, j) is the number of times character e i was aligned to c j .

from the affinity matrix conditional probabilities P (e i |c j ) can be estimated as Alignment j = θ(i) between {e i } and {c j } that maximizes probability is also the same alignment that minimizes alignment distance D: equations (2) and (5) are the same when we have the distance function d(e i , c j ) = − log P (c j |e i ).


We mark the EC name pairs as genuine and the rest 112,622 name pairs that do not follow the Chinese phonetic rules as false transliterations, thus creating the ground truth labels for an English-Chinese transliteration validation experiment.

lDC05 has 560,768 genuine transliteration pairs and 112,622 false ones.


Unfortunately, large quantities of parallel data are not readily available for some languages pairs, therefore limiting the potential use of current SMT systems.

for speech translation, the translation task often focuses on a specific domain such as the travel domain.


The suggested algorithm has similar properties as the algorithm presented in .

it has the same upper bound on the number of line segments that constitute the envelope at the root node, i.e, the size of this envelope is guaranteed to be no larger than the number of edges in the transformed hypergraph.


We now present an alternate approximate procedure which can avoid this enumeration making the resulting algorithm much faster than FSAMBR.

the key idea behind this new algorithm is to rewrite the n-gram posterior probability (Equation 4) as follows: where f (e, w, E) is a score assigned to edge e on path E containing n-gram w: e precedes e on E 0 otherwise 6for each path E, we count the edge that contributes n-gram w and has the highest edge posterior probability relative to its predecessors on the path E; there is exactly one such edge on each lattice path E. We note that f (e, w, E) relies on the full path E which means that it cannot be computed based on local statistics.


We hypothesize that the default MBR parameters are sub-optimal for some language pairs and that MERT helps to find better parameter settings.

mERT avoids the need for manually tuning these parameters by language pair.


As common in previous work (e.g., Shim and Kim 2002), we assume that our generator operates in an interactive context.

the user supplies the topic of the story and its desired length.


story generation amounts to traversing the tree and selecting the nodes with the highest score our story generator applies two distinct search procedures.


For every entity in our training corpus we extract event chains similar to those proposed by Chambers and Jurafsky (2008).

we identify the events every entity relates to and record their (partial) order.


We apply the method described in Section 3.1 to the downloaded web pages.

we find all instances of words in the lexicon, extract their targets, and mask the words with their polarities, yielding polarity-target pairs.


We recognize such discourse constructs using the Penn Discourse Treebank (Prasad et al., 2007) list of discourse connectives.

we use the list of connectives from the Concession and Contra-expectation category.


The opinions occurring in the conceded part are interpreted in reverse.

the weights corresponding to the sides w j and u j are interchanged in equation 4.


Each noun k is assigned to the topic with the higher PMI score.

if P M I(topic 1 ,k) > P M I(topic 2 ,k) ⇒k= topic 1 and if 4 http://cwl-projects.cogsci.rpi.edu/msr/ P M I(topic 2 ,k) > P M I(topic 1 ,k) ⇒k= topic 2 Next, the polarity-target pairs are found for the post, as before, and Equations 10 and 11 are used to assign a side to the post as in the OpTopic system, except that here, related nouns are also counted as instances of their associated topics.


DauméIII and Marcu (2006) introduce a statistical formulation of this problem in terms of a simple mixture model.

several previous studies focus on the problem of cross-lingual text classification, which can be considered as a special case of general cross-domain text classification.


The term-document matrix is typically very sparse with z nm non-zero entries while k is typically also much smaller than n, m. By using sparse matrix multiplications and avoiding dense intermediate matrices, the updates can be very efficiently and easily implemented.

updating F, S, G each takes O(k 2 (m + n) + kz) time per iteration which scales linearly with the dimensions and density of the data matrix.


Both of these methods have been widely used in sentiment analysis.

the use of SVMs in (Pang et al., 2002) initially sparked interest in using machine learning methods for sentiment classification.


To verify the effectiveness of our term weighting schemes in experimental settings of the datadriven approach, we carry out a set of simple experiments with ML classifiers.

we explore the statistical term weighting features of the word generation model with Support Vector machine (SVM), faithfully reproducing previous work as closely as possible (Pang et al., 2002).


Both tables show that abstract terms and concrete terms favor different sets of features.

contextual, co-occurrence, pattern, and syntactic features work well for terms at L 4 -L 6 , i.e., concrete terms; co-occurrence works well for terms at L 2 -L 3, i.e., abstract terms.


The question addressed in this article is: How problematic is learning from training data with annotation noise?

we are interested in estimating the degree to which performance on easy instances at test time can be hurt by the presence of hard instances in training data.


If we read these tables column-wise, thereby taking the more linguistically-inspired labels in VerbNet to be the reference labels, we observe that the labels in PropBank are especially concentrated on those labels that linguistically would be considered similar.

in Table 3 A0 mostly groups together Agents and Instrumental Causes; A1 mostly refers to Themes and Patients; while A2 refers to Goals and Themes.


It is often claimed in the literature that semantic roles are better de-scribed by feature bundles.

the features sentience and volition have been shown to be useful in distinguishing Proto-Agents from Proto-Patients (Dowty, 1991).


In fact, this graph produces a negative Pearson correlation coefficient between system and segment levels of 0.44.

depending on how the correlation is computed, the relative predictive power of metrics can swap.


We now investigate to what extent a significant system improvement according to the metric implies a significant improvement according to human assessors, and viceversa.

: are the metrics able to detect any quality improvement?


Note that the combined metric, ULC (the circle in the figure), achieves results comparable to n-gram based metrics with this test 3 .

combining linguistic and ngram based metrics preserves the good behavior of n-gram based metrics in this test.


3 Notice that we just have 75 significant improvement samples, so small differences in SIP or SIR have no relevance The issue tackled in this section is to what extent a very low or high score according to the metric is reliable for detecting extreme cases (very good or very bad translations).

note that detecting wrong translations is crucial in order to analyze the system drawbacks.


Being Q h (t) and Q m (t) the human and metric assessed quality for the translation t, and being rank h (t) and rank m (t) the rank of the translation t according to humans and the metric, the normalized metric assessed quality is: In order to analyze the reliability of metrics when identifying wrong or high quality translations, we look for contradictory results between the metric and the assessments.

we look for metric errors in which the quality estimated by the metric is low (Q Nm (t) ≤ 3) but the quality assigned by assessors is high (Q h (t) ≥ 5) or viceversa (Q Nm (t) ≥ 7 and Q h (t) ≤ 4).


We integrate the SDB model into phrase-based SMT to help decoder perform syntax-driven phrase translation.

we add a new feature into the log-linear translation model: P SDB (b|T, τ (.)).


Our formulation is able to handle non-local output features in an efficient manner; not only is it compatible with prior knowledge encoded as hard constraints, it can also learn soft constraints from data.

our model is able to learn correlations among neighboring arcs (siblings and grandparents), word valency, and tendencies toward nearlyprojective parses.


Our formulations offer the following comparative advantages: • The numbers of variables and constraints are polynomial in the sentence length, as opposed to requiring exponentially many constraints, eliminating the need for incremental procedures like the cutting-plane algorithm; • LP relaxations permit fast online discriminative training of the constrained model; • Soft constraints may be automatically learned from data.

our formulations handle higher-order arc interactions (like siblings and grandparents), model word valency, and can learn to favor nearly-projective parses.


Ideally, o should always return the optimal transition t for a given configuration c, but all we require formally is that it respects the preconditions of transitions in T .

if o(c) = t then t is permissible in c. Given an oracle o, deterministic transition-based parsing can be achieved by the following simple algorithm: Starting in the initial configuration c s (x), the parser repeatedly calls the oracle function o for the current configuration c and updates c according to the oracle transition t. The iteration stops when a terminal configuration is reached.


It is worth pointing out that, although the system described in Nivre (2008b) uses four transitions bearing the same names as the transitions of S u , the two systems are not equivalent.

the system of Nivre (2008b) is sound but not complete for the class of all dependency trees.


Unfortunately, there is no straightforward generalization of the method of  to the two edge marginal problem.

laplace expansion generalizes to second-order matrix minors, but it is not clear how to compute secondorder cofactors from the inverse Kirchoff matrix alone (c.f.


The first set of constraints specify the most frequent head tag, attachment direction, and distance combinations for each child tag.

we select oracle constraints of the type (parent-CPOS,child-CPOS,direction,distance) 11 .


In this paper, we present a flexible learning framework for transferring dependency grammars via bitext using the posterior regularization framework (Graça et al., 2008).

we address challenges (1) and (2) by avoiding commitment to an entire projected parse tree in the target language during training.


In grammar transfer, our basic constraint is of the form: the expected proportion of conserved edges in a sentence pair is at least η (the exact proportion we used was 0.9, which was determined using unlabeled data as described in Section 5).

let C x be the set of directed edges projected from English for a given sentence x, then given a parse z, the proportion of conserved edges is The posterior regularization framework (Graça et al., 2008) was originally defined for generative unsupervised learning.


The first rule sets main verbs to dominate auxiliary verbs.

whenever an auxiliary precedes a main verb the main verb becomes its parent and adopts its children; if there is only one main verb it becomes the root of the sentence; main verbs also become Figure 4: An example where transfer fails because of different handling of reflexives and nonfinite clauses.


2) Traditional alignment method evaluates the alignment probability coarsely.

we don't know exactly which target word(s) should be aligned to for the source word.


Sampling with PDF-500 results in more accurate lexicons over the first 1000 terms than the other sampling methods for WMEB.

wMEB is more accurate with the unsupervised seeds than the S gold and S hand (81.0% vs 78.6% and 78.6%).


In different phases of WMEB+DIST we reduce semantic drift significantly.

at 801-1000, ANTI increase by 46% to 87.5% and MUTN by 59% to 92.0%.


Second, the amount of engineering required to build our system is similar to the efforts required for using an off-the-shelf classifier: we only need to define features (in terms of formulae) and provide input data in the correct format.

2 we do not need to manually construct ILPs for each document we encounter.


In this work we are focusing on the three tasks of TempEval, and our running hypothesis is that they should be tackled jointly.

instead of learning separate probabilistic models for each task, we want to learn a single one for all three tasks.


the j-th row in the matrix R) using a mapping Θ : D → R N .

each chained entity is represented as a vector of its relation strengths with all the entities.


Kernelization (Schölkopf and Smola, 2002) is a machine learning technique to transform patterns in the data space to a high-dimensional feature space so that the structure of the data can be more easily and adequately discovered.

a nonlinear transformation Φ maps data in R N to H of possibly infinite dimensions (Hilbert space).


Note that we used support vector machines (SVMs) in our experiments and (the absolute value of) the distance between a sample and the hyperplane determined by the SVMs was used as confidence value r. The training data are denoted by L ⊂ X ×CL, and we denote the learning by function LEARN ; if classifier c is trained by training data L, then c = LEARN (L).

we denote the training sets for S and T that are manually prepared by L S and L T , respectively.


The results were encouraging because the classifiers showed better performance than their initial ones in every setting.

a strong classifier always taught a weak classifier well, and the strong one also got help from the weak one, regardless of the size of the training data with which the weaker one learned.


Then, the L1 penalty is applied to the weight to the extent that it does not change its sign.

the weight is clipped when it crosses zero.


We investigate the use of distributional representations, which model the probability distribution of a word's context, as techniques for finding smoothed representations of word sequences.

we use the distributional representations to share information across unannotated examples of the same word type.


We provide empirical evidence that shows how distributional representations improve sequencelabeling in the face of data sparsity.

we investigate empirically the effects of our smoothing techniques on two sequence-labeling tasks, POS tagging and chunking, to answer the following: 1.


This example provides the intuition for our approach to smoothing: we seek to share information about the contexts of a word across multiple instances of the word, in order to provide more information about words that are rarely or never seen in training.

we seek to represent each word by a distribution over its contexts, and then provide the learning algorithm with features computed from this distribution.


To supply a sequence-labeling algorithm with information from these distributional representations, we compute real-valued features of the context distributions.

for every word x i in a sequence, we provide the sequence labeler with a set of features of the left and right contexts indexed by v ∈ V: For example, the left context for "reformulated" in our example above would contain a nonzero probability for the word "of."


With this search space representation, we can consistently handle unknown words with character-level nodes.

we use word-level nodes to identify known words and character-level nodes to identify unknown words.


MIRA combines the advantages of margin-based and perceptron-style learning with an optimization scheme.

we use a generalized version of MIRA (Crammer et al., 2005;McDonald, 2006) that can incorporate k-best decoding in the update procedure.


2 A dependency in Japanese is a modification relation in which a modifier bunsetsu depends on a modified bunsetsu.

the modifier bunsetsu and the modified bunsetsu work as modifier and modifyee, respectively.


We performed a cross-validation experiment by using 16 discourses.

we repeated the experiment, in which we used one discourse from among 16 discourses as the test data and the others as the learning data, 16 times.


Consider text summarization first: many well-known models such as MMR (Carbonell and Goldstein, 1998) and MEAD (Radev et al., 2004) rely on the reoccurrence statistics of words.

if we switch any word w 1 with another word w 2 across an entire corpus, the ranking of extracts (often sentences) will be unaffected, because no wordspecific knowledge is involved.


After the multiple DTW paths are calculated, the optimal subpath on each is then detected in order to find the local alignments where the similarity is maximal, which is where we expect actual matched phrases to occur.

for a given path P = p 0 , p 2 , ..., p K , the optimal subpath is defined to be a continuous subpath, , 0 ≤ n ≤ m ≤ k, and m − n + 1 ≥ L. the subpath is at least as long as L and has the maximal average similarity.


However, these measures are all functions of features of e .

they can be expressed as S(e; φ(e )) for a feature mapping φ : E → R n .


Following the structure of Equation 1, we can choose a translation e based on the feature expectations of e .

we can choosẽ e = arg max e∈E S(e; E P(e |f ) φ(e ) ).


Forest-based consensus decoding leverages information about the correct translation from the entire forest.

consensus decoding with BLEU chooses translations using n-gram count expectations E[c(e, t)].


Despite different inside, their derivations must begin with f and end with e. This situation remains the same for derivations between a source substring f j i and its partial translation t during joint decoding: where d might come from multiple models.

derivations from multiple models could be brought together for computing the probability of one partial translation.


This could be the result of less diversified outputs from co-decoding than those from baseline decoders.

the results for hypothesis selection are only slightly better than the best system in co-decoding.


Statistical models in machine translation exhibit spurious ambiguity.

the probability of an output string is split among many distinct derivations (e.g., trees or segmentations).


To approximate the intractable decoding problem of (2), most MT systems (Koehn et al., 2003;Chiang, 2007) use a simple Viterbi approximation, Clearly, (5) replaces the sum in (2) with a max.

it approximates the probability of a translation string by the probability of its mostprobable derivation.


In fact, if p were the empirical distribution over strings in a training corpus, then q * of (10) is just the maximum-likelihood n-gram model-whose parameters, trivially, are just unsmoothed ratios of the n-gram and (n−1)-gram counts in the training corpus.


Figure 4 shows an efficient version that exploits the packed-forest structure of HG(x) in computing the expected counts.

it first runs the inside-outside procedure, which annotates each node (say v) with both an inside weight β(v) and an outside weight α(v).


Lastly, note that Viterbi and variational approximation are different ways to approximate the exact probability p(y | x), and each of them has pros and cons.

viterbi approximation uses the correct probability of one complete derivation, but ignores most of the derivations in the hypergraph.


For each SAPT node that does not combine a predicate with a legal argument, a macro-predicate is formed by merging all predicates on the paths from the child predicates to their lowest common ancestor (LCA) in the MR parse.

a child MR becomes an argument of the macro-predicate if it is complete (i.e.


Then the GIZA++ implementation (Och and Ney, 2003) of IBM Model 5 is used to generate the five best word/predicate alignments from the corpus of NL sentences each paired with the predicate sequence for its MR. After predicates are assigned to words using word alignment, for each alignment of a training example and its syntactic parse, a SAPT is generated for composing the correct MR using the processes discussed in Sections 3 and 4.

a semantic label is assigned to each internal node of each SAPT, so that the MRs of its children are composed correctly according to the MR for this example.


In this paper, we extend LDA to model structural dependencies between latent annotated concepts (cf.

(Li and McCallum, 2006;Sivic et al., 2008)); we fix the concept structure to correspond to the WN Is-A hierarchy.


When prior attribute rank information (Per-Node and CE scores) from the baseline extractions is not incorporated, all LDA-based models outperform the unranked baseline (Table 1).

lDA yields a 17% reduction in error (MAP)


Following , the regression model can be used to decode mental states.

for each regression model, the estimated regression weights can be used to generate the predicted activity for each word.


Table 6 shows a pattern similar to that seen in section 4 is observed for the attribute-specifying adjectives.

the noun model outperformed the adjective model and the multiplicative model outperformed the additive model when using attributespecifying adjectives.


There have been recently increasing concerns with the need for anaphora resolution to make NLP applications such as IE and MT more reliable.

for languages such as Japanese, anaphora resolution is crucial for resolving a phrase in a text to its referent since phrases, especially nominative arguments of predicates, are frequently omitted by anaphoric functions in discourse (Iida et al., 2007b).


MUC-6 (1995), ACE NIST (2004)) and their use in many formal evaluations, as a field we can make surprisingly few conclusive statements about the state-of-theart in NP coreference resolution.

it remains difficult to assess the effectiveness of different coreference resolution approaches, even in relative terms.


Previous work has studied performance differences between pronominal anaphora, proper names, and common nouns, but we aim to dig deeper into subclasses of each of these groups.

we distinguish between proper and common nouns that can be resolved via string matching, versus those that have no antecedent with a matching string.


7 Although annotated in the PDTB, throughout this paper I have ignored the S-medial discourse adverbial also, as in "John also eats fish", since such instances are better regarded as presuppositional.

as well as a textual antecedent, they can be licensed through inference (e.g.


For Comparison and Contingency, we analyze the behavior of word pair features under several different settings.

we want to address two important related questions raised in recent work by others: (i) is unannotated data from explicits useful for training models that disambiguate implicit discourse relations and (ii) are explicit and implicit relations intrinsically different from each other.


These two measurements form the basis for all the measurements that are used by the FS methods throughout this paper.

we show that DF and MI are using the first and second measurement respectively.


We use 9-fold cross validation to help learning the parameter λ so as to avoid over-fitting.

we run nine times by using every 8 fold documents as a new training data set and the remaining one fold documents as a development data set.


In light of the difficulties posed by ambiguous reviews, we differentiate between ambiguous and unambiguous reviews in our classification process by addressing the task of semi-supervised polarity classification via a "mine the easy, classify the hard" approach.

we propose a novel system architecture where we first automatically identify and label the unambiguous (i.e., "easy") reviews, then handle the ambiguous (i.e., "hard") reviews using a discriminative learner to bootstrap from the automatically labeled unambiguous reviews and a small number of manually labeled reviews that are identified by an active learner.


We begin by preprocessing the reviews to be classified.

we tokenize and downcase each review and represent it as a vector of unigrams, using frequency as presence.


A common approach is to set the threshold to zero.

all points whose value in the second eigenvector is positive are classified as positive, and the remaining points are classified as negative.


According to the second eigenvector, it should be assigned to the "negative" cluster; but if feature #4 were irrelevant, it should be assigned to the "positive" cluster.

the ability to determine the relevance of each feature is crucial to the accurate clustering of the ambiguous data points.


As a result, we apply active learning (Cohn et al., 1994) to identify the ambiguous reviews.

we train a discriminative classifier using the support vector machine (SVM) learning algorithm (Joachims, 1999) on the set of unambiguous reviews, and then apply the resulting classifier to all the reviews in the training folds 4 that are not seeds.


(2000), Schohn and Cohn (2000), Tong and Koller (2002)), we define the uncertainty of a data point as its distance from the separating hyperplane.

points that are closer to the hyperplane are more uncertain than those that are farther away.


Hence, instead of training just one SVM classifier, we aim to reduce classification errors by training an ensemble of five classifiers, each of which uses all 100 manually labeled reviews and a different subset of the 500 automatically labeled reviews.

we partition the 500 automatically labeled reviews into five equal-sized sets as follows.


Our last baseline implements the active learning procedure as described in Tong and Koller (2002).

we begin by training an inductive SVM on one labeled example from each class, iteratively labeling the most uncertain unlabeled point on each side of the hyperplane and re-training the SVM until 100 points are labeled.


Results of our approach are shown in rows 4-6 of Table 2.

rows 4 and 5 show the results of the SVM classifier when it is trained on the labeled data obtained after the first step (unsupervised extraction of unambiguous reviews) and the second step (active learning), respectively.


To further understand the role of seeds, we experimented with a version of our approach that bootstraps from no seeds.

we used the 500 seeds to guide the selection of active learning points, but trained a transductive SVM using only the active learning points as labeled data (and the rest as unlabeled data).


Our key idea is to distinguish between unambiguous, easy-tomine reviews and ambiguous, hard-to-classify reviews.

given a set of reviews, we applied (1) an unsupervised algorithm to identify and classify those that are unambiguous, (2) an active learner that is trained solely on automatically labeled unambiguous reviews to identify a small number of prototypical ambiguous reviews for manual labeling, and (3) an ensembled transductive learner to train a sophisticated classifier on the reviews labeled so far to handle the ambiguous reviews.


In contrast, the goal of this paper is to model and classify such speaker attributes from only the latent information found in textual transcripts.

we are interested in modeling and classifying biographic at-tributes such as gender and age based on lexical and discourse factors including lexical choice, mean utterance length, patterns of participation in the conversation and filler word usage.


Phrase Identification(PI): Our PI module undertakes basic syntactic analysis (shallow parsing) and establishes simple, un-embedded linguistic structures such as noun-phrases (NN), basic prepositional phrases (PP) or verb groups (VG).

pI module is based on 56 different semantic structures identified in Standford dependency parser in order to extract meaningful compound words from sentences, e.g., "They heard high pitched cries.".


And ω(v i , o j ) is described as an indicative function.

if word o j appears in the sentence is equal to 1.


Some new words are clearly good for human sense and definitely convey novel semantic information, but they can be useless for speech recognition.

character n-gram may handle these words equally well due to the low ambiguities with other words.


Moreover, the same procedure can be used in the construction.

beginning with only characters in the lexicon and using the training data to alter the current lexicon in each iteration.


One of the challenges to reducing the WER of ASR transcriptions of lecture recordings is the lack of manual transcripts on which to train various ASR improvements.

for onehour lectures given by different lecturers (such as, for example, invited presentations), it is often impractical to manually transcribe parts of the lecture that would be useful as training or development data.


The experimental evaluation carried out over eleven lectures from three different courses and instructors shows that this amount of manual transcription can be sufficient to further improve a lecture-specific ASR system.

we demonstrated that a true WERbased scoring function for the TBL algorithm is both feasible and effective with a limited amount of training data and no development data.


The likelihood term, P (x|g), is the probability of the training sentence pairs under the grammar, while the prior term, P (g), describes our initial expectations about what consitutes a plausible grammar.

we incorporate priors encoding our preference for a briefer and more succinct grammar, namely that: (a) the grammar should be small, with few rules rewriting each non-terminal; and (b) terminal rules which specify phrasal translation correspondence should be small, with few symbols on their right hand side.


This distribution has a strong bias towards short strings.

note that generally a string of length k will be less probable than two of length k 2 , a property very useful for finding 'minimal' translation units.


Developing better context-models to be applied on the source is expected to further improve our method's performance.

we suggest taking into account the prior likelihood that a rule is correct as part of the model score.


What is worth noting is that the number of sentences at levels 4 and 5 in terms of fluency and adequacy are much higher in case of the model that uses semantic relations.

the use of semantic relations, in combination with syntactic reordering, produces many more sentences that are reasonably or even perfectly fluent and convey most or all of the meaning.


We believe, however, that despite this shortcoming, we can still take advantage of some of the insights gained from looking at the influence of IS on word order.

we look at the problem from a more general perspective by computing an asymmetry ratio for each pair of IS categories.


Here, the frequency of a unit is measured using the language model mentioned above 3 .

the langauge model assigns a score score lm (•) for each unit and the unit with larger score is viewed as more frequent.


We introduce cause identification, a new problem involving classification of incident reports in the aviation domain.

given a set of pre-defined causes, a cause identification system seeks to identify all and only those causes that can explain why the aviation incident described in a given report occurred.


The difficulty of a text classification task depends on various factors, but typically, the task can be difficult if (1) the amount of labeled data available for learning the task is small; (2) it involves multiple classes; (3) it involves multilabel categorization, where more than one label can be assigned to each document; (4) the class distributions are skewed, with some categories significantly outnumbering the others; and (5) the documents belong to the same domain (e.g., movie review classification).

when the documents to be classified are from the same domain, they tend to be more similar to each other with respect to word usage, thus making the classes less easily separable.


Each report is a free text narrative that describes not only why an incident happened, but also what happened, where it happened, how the reporter felt about the incident, the reporter's opinions of other people involved in the incident, and any other comments the reporter cared to include.

a lot of information in the report is irrelevant to (and thus complicates) the task of cause identification.


The third argument, U , is the unlabeled set of documents, which consists of all but the documents in the training set.

u contains the documents in the development and test sets.


Whichever baseline is used, we need to reserve one of the five folds to tune the parameter k in our cross validation experiments.

if the second baseline is used, we will tune CT and k jointly on the development data using the local search algorithm described previously, where we adjust the values of both CT and k for one of the 14 classifiers in each step of the search process to optimize the overall F-measure score.


to a large drop in recall accompanied by a smaller gain in precision.

for the four non-minority classes, the benefits obtained from using the bootstrapped documents can also be obtained by simply adjusting the CT.


(2007) address the problem of class skewness in text classification.

they first under-sample the majority classes, and then bootstrap the classifier trained on the under-sampled data using unlabeled documents collected from the Web.


Naive Algorithm: In this algorithm, we scan each list L i and query the index on each term appearing in L i .

the returned questions are added to a collection C. the collection C is called the candidate set.


This combination provides a framework that benefits from the advantages of both generative and discriminative models.

when there is no or a very small amount of labeled data, a parser could still work by using unsupervised learning approaches to learn the rules, or by simply using a set of hand-built rules (as we did above for the task of semantic tagging).


In this paper, based on the observation that bilingual data in many web pages appear collectively following similar patterns, an adaptive pattern-based bilingual data mining method is proposed.

given a web page, the method contains four steps: 1) preprocessing: parse the web page into a DOM tree and segment the inner text of each node into snippets; 2) seed mining: identify potential translation pairs (seeds) using a word based alignment model which takes both translation and transliteration into consideration; 3) pattern learning: learn generalized patterns with the identified seeds; 4) pattern based mining: extract all bilingual data in the page using the learned patterns.


Therefore in this paper, we propose a pattern-based approach: learning patterns adaptively from collective bilingual pages instead of using the parenthesis pattern, then using the learned patterns to extract translation pairs from corresponding web pages.

our approach contains four steps: 1) Preprocessing: parse the web page into a DOM tree and segment the inner text of each node into snippets; 2) Seed mining: identify potential translation pairs (seeds) using an alignment model which takes both translation and transliteration into consideration; 3) Pattern learning: learn generalized patterns with the identified seeds; 4) Pattern based mining: extract all bilingual data in the page using the learnt patterns.


We define simulation accuracy as: Correctly predicted human user actions Total number of human user actions (5) However, since our simulation model is a probabilistic model, the model will take an action stochastically after the same tutor turn.

we need to take into account the probability for the simulation to predict the right human user action.


In order to minimize the number of redundant tree sequence pairs, we limit the # of gaps of non-contiguous tree se-quence pairs to be 0 in either source or target side.

we only allow one side to be noncontiguous (either source or target side) to partially reserve its syntactic and semantic cohesion 4 .


(2005), we utilize a loss that decomposes across alignments.

for each alignment cell (i, j) which is not a possible alignment in a * , we incur a loss of 1 when a ij = a * ij ; note that if (i, j) is a possible alignment, our loss is indifferent to its presence in the proposal alignment.


It furthermore led to better translation scores for Chinese and Arabic documents with different genres.

it improved the translation scores of the tail documents by 0.5-1.4 points measured by the combined metric of (TER-BLEU)/2.


Since a cell may represent an empty word, a cell sequence may represent an n-gram where We denote |S(i, i )| to be the length of n-gram represented by a particular cell sequence S(i, i ).

all the cell sequences S(i, i ) can be classified, with respect to the length of corresponding n-grams, into a set of parameters where each element (with a particular value of n) has the probability The probability of the transition from i to i is: the transition probability of incremental IHMM is a weighted sum of probabilities of 'ngram jumping', defined as conventional IHMM distortion probabilities.


Therefore a length limit L is imposed such that for all state transitions where |i − i| ≤ L, the transition probability is calculated as equation 4, otherwise it is calculated by: for some q between i and i .

the probability of longer state transition is estimated in terms of the probabilities of transitions shorter or equal to the length limit.


We tried the values 5 to 8 for L and the variation of performance is less than 0.1 BLEU point.

distortion model 1 cannot be improved by tuning L. The similar BLEU scores as shown in Table 3 implies that the incorporation of more word sequences in distortion model 1 does not lead to extra improvement.


In contrast, the equations in section 4 shows that CD-IHMM calculates the weighted sum of the logarithm of those probabilities of the component translations.

inciHMM2 makes use of the sum of probabilities whereas CD-iHMM makes use of the product of probabilities.


If the heuristic is consistent, then A * guarantees that whenever an inside item comes off the agenda, its weight is its true Viterbi inside score (Klein and Manning, 2003c).

this guarantee implies that the goal item I(G, 0, n) will be popped with the score of the 1-best parse of the sentence.


Because of the context freedom of the grammar, any consistent heuristic for inside edge items usable in 1-best A * is also consistent for inside derivation items (and vice versa).

the 1-best Viterbi outside score for an edge is a "perfect" heuristic for any derivation of that edge.


While formulated very differently, one limiting case of our algorithm relates closely to the EXH algorithm of Huang and Chiang (2005).

if all inside items are processed before any derivation items, the subsequent number of derivation items and outside items popped by KA * is nearly identical to the number popped by EXH in our experiments (both algorithms have the same ordering bounds on which derivation items are popped).


For instance, in an edit graph of size 8 × 8, there is only one path that goes through the vertex at the top-right corner, while more than 3,600 paths pass through the vertex at the center of the graph.

the features associated with the center vertex receives 3,600 times more weights than those at the top-right corner after averaging.


We introduce here a specialized representation for p. Let $ be a fresh symbol that does not occur in p. We define the characteristic string of p as the string where each α j is obtained from α j by removing all the occurrences of symbols in V T .

consider now some occurrence A i of a nonterminal symbol in the right-hand side of p. We define the position set is a variable of the form x i,h for some h. We have σ we are decomposing X into the union of k intervals, with k as small as possible.


Furthermore, in a TT-MCTAG derivation the argument trees must either adjoin directly to their head tree, or they must be linked in the derivation tree to an elementary tree that attaches to the head tree, by means of a chain of adjunctions at root nodes.

in the corresponding TAG derivation tree, the head tree must dominate the argument trees in such a way that all positions on the path between them, except the first one, must be labeled by ε.


For our parsing algorithm, we want to avoid grouping the instances of elementary trees in a derivation tree into tuple instances.

we want to check whether a TAG derivation tree is a valid TT-MCTAG derivation tree without deciding, for every occurrence of some argument β, which of the h(β)-nodes represents its head.


This observation gives some intuition about how to separate general features from typespecific features.

here we consider two hypotheses regarding the generality of different kinds of features.


We can also combine the automatic way of setting F with human guidance.

we still follow the first automatic procedure to choose general features, but we then filter out any hypothesized type-specific feature from the set of general features chosen by the automatic procedure.


Therefore, we do not parse information from the Web corpus, but from well written texts.

we specifically examine unsupervised relation extraction from existing texts of Wikipedia articles.


3 according to the importance of each phrase by adding multiple free parameters to the retrieval model.

we replace λ with wellknown logistic function, which allows both numerical and categorical variables as input, whereas the output is bounded to values between 0 and 1.